Full Stack System Architecture

The interface layer that renders in the browser or on a device. It handles UI state, user interactions, routing between pages, and communicating with backend services via API calls. At FAANG scale this is a complex, independently deployable application — not a collection of HTML files. Mobile apps (iOS and Android) are separate codebases with their own release cycles, app store review processes, and platform-specific constraints.

Static assets — JavaScript bundles, images, fonts, HTML — are cached at data centres globally, geographically close to users, so they load fast regardless of where the user is. Edge functions allow code to run at these global nodes without hitting origin servers. DDoS protection and SSL/TLS termination also live here. This is the first layer the internet touches before your servers do.

Every request from the frontend passes through the API gateway before reaching any backend service. It enforces rate limits, routes requests to the correct service, validates tokens, and distributes traffic across multiple server instances so no single server gets overwhelmed. This is also where API versioning is enforced — v1 requests go to old services, v2 requests go to new ones.

Two distinct problems that get conflated. Authentication is proving identity — you are who you say you are. Authorisation is enforcing permissions — you are allowed to do what you're trying to do. At FAANG scale these are separate services. Auth includes social login, enterprise single sign-on (SSO), multi-factor authentication, session token management, and token refresh and revocation flows. Role-based access control (RBAC) defines what each user type can see and do.

This is where your product's core functionality lives. At early stage this is often a monolith — one deployable application containing all business logic. At FAANG scale it is decomposed into dozens to hundreds of microservices, each owned by a separate team, independently deployable, communicating via internal APIs or a service mesh. Each microservice owns its own data store. This layer contains more code and engineering time than any other.

Not everything needs to happen synchronously in a request-response cycle. Sending an email, processing a video upload, running a nightly report, triggering a webhook — these happen asynchronously. A message queue decouples the service that produces work from the service that processes it. If a downstream service goes down, the queue holds work until it recovers. This is the difference between a resilient system and a brittle one.

Reading from a database on every request is slow and expensive. The caching layer stores the results of expensive operations — database queries, API calls, computed values — in memory so they can be retrieved in microseconds rather than milliseconds. Redis is the most widely used: it serves as a cache, session store, rate limiter, and pub/sub broker simultaneously. Cache misses fall back to the database. Cache invalidation — knowing when to expire cached data — is one of the genuinely hard problems in distributed systems.

Relational databases are not built for search. A search layer is a dedicated system that indexes content for fast, relevance-ranked retrieval — including typo tolerance, synonyms, faceted filtering, and autocomplete. AI has changed search fundamentally: vector databases enable semantic similarity search — finding results by meaning, not exact keywords — which powers recommendation systems, RAG pipelines, and AI-native search experiences.

Where persistent data lives — the source of truth for your application. Database type is chosen based on data model and access patterns. Relational databases handle structured, transactional data with complex relationships. NoSQL handles flexible schemas, extreme scale, or access patterns that don't fit tables. FAANG-scale apps run multiple database types across different services. Replication (primary/replica) distributes read traffic. Sharding distributes write traffic across nodes. Connection pooling manages the finite number of database connections efficiently.

Databases store structured data. Object storage stores unstructured binary files — profile photos, uploaded PDFs, audio, video, CSV exports, trained ML model weights. At scale, raw files are processed before storage: images are resized and compressed into multiple format variants, videos are transcoded into streaming formats, documents are scanned for malicious content. All processed assets are then served through the CDN layer — never directly from the storage bucket.

AI in production is not one thing — it is a stack. The full ML layer includes: data pipelines that build training datasets, a feature store that computes and serves ML features in real time, model training infrastructure, a model registry for version control, model serving infrastructure for inference, evaluation systems to measure model quality, and A/B testing frameworks to compare model versions. For LLM-based products, add prompt management, RAG pipelines, guardrails, and output evaluation. This is the most complex and fastest-changing layer in modern web applications.

A dedicated notification system handles all outbound communication across every channel. At scale this is not simply calling an email API from the backend. It includes channel routing, user preference management, frequency capping (preventing spam), delivery tracking, bounce and unsubscribe management, and regulatory compliance with CAN-SPAM and GDPR. Webhook delivery to third-party systems lives here with retry logic, signature verification, and delivery logs.

Payment infrastructure is far more complex than dropping in a checkout form. A production billing layer handles: card processing with 3DS authentication, subscription lifecycle management including trials and failed payment recovery, invoice generation, global tax calculation across jurisdictions, refund logic, and fraud detection. PCI DSS compliance means card data must never touch your servers — it is tokenised entirely by the payment processor. A failed payment recovery system (dunning) alone can recover 20-30% of involuntary churn.

At FAANG scale, features are never switched on for everyone at once. Feature flags allow code to exist in production while only activating for specific users — internal testers, a 1% canary group, a specific market, a specific pricing tier. A/B testing infrastructure runs controlled experiments to measure whether a change improves a target metric. The most sophisticated systems run hundreds of simultaneous experiments with statistical rigour built into the platform. Engineers at Meta and Google deploy to production dozens of times per day behind flags.

The operational database is optimised for transactions, not analysis. The data layer is a separate system optimised for analytical queries across the full history of your data. Events from the application are streamed into a data warehouse via ETL/ELT pipelines. BI tools query the warehouse to produce dashboards. Data scientists run analyses that drive product decisions. At FAANG scale this is a separate engineering discipline — Data Engineering — with its own teams, tooling, and infrastructure entirely separate from product engineering.

Infrastructure is the platform all other layers run on. At FAANG scale it is entirely code-defined — every server, network rule, database instance, and permission is declared in version-controlled configuration files, not clicked into existence in a web console. Kubernetes orchestrates containers across fleets of machines. CI/CD pipelines automatically test, build, and deploy code changes many times per day — engineers at Meta and Google each deploy to production thousands of times daily across the organisation.

You cannot manage what you cannot see. Observability is the ability to understand the internal state of a system from its external outputs. The three pillars are logs — a record of what happened, metrics — measurements of system behaviour over time, and traces — following a single request across every service it touches. At FAANG scale this is a 24/7 engineering discipline: on-call rotations, automated alerting with runbooks, and blameless post-mortems for every significant outage. Without this layer you are flying blind in production.

Security is a posture applied across every layer above, but it also has dedicated infrastructure. A Web Application Firewall blocks malicious traffic patterns. A SIEM aggregates security events and detects anomalies. Vulnerability scanners continuously check code dependencies and infrastructure for known exploits. Penetration testing finds vulnerabilities before attackers do. Compliance certifications — SOC 2, GDPR, HIPAA, ISO 27001 — impose specific controls across the entire stack, and in B2B they are often a hard requirement to close enterprise deals.